I would like to point out that I am new to this field, so if I am not clear please forgive me (and correct me).
I set up a DoE (Design of Experiment) with 11 inputs and 121 runs. I used a STOA (Strength-Two Orthogonal Array) to fill the domain and I ran my test. I measured 5 outputs, so at the end I have a
121x11 input array and a
121x5 output array.
My task is to generate a RSM (Response Surface Model) from these data and I am currently using the kriging algorithm but I do not get good results in terms of accuracy (the RMSE (Root Mean Square Error) is quite high).
My questions are:
- since kriging comes out from geostatistics applications, it is correct to say that it works well just with 2D or 3D spaces and it loose its accuracy with high dimensional spaces? Why or why not?
- given my training set, how can I choose the best algorithm to fit my RSM?
EDIT1: @whuber - I got your concerns. I actually have some lack about data analysis and I want to look into it. Where can I start? Do you know some god books or websites?
EDIT2: @GeoMatt22 -
- I performed a Leave-One-Out Cross Validation and I plotted the Actual vs Predicted graph. For some outputs the distribution of points is rather spread and the RMSE is high. I suppose I should do some data analysis. What should I look at? Variance of data? Covariance?
- Goal is optimization.
EDIT3: @David Kozak - Thanks for your observations. I will take a look to the Rasmussen and Williams.